The application of Uniform Manifold Approximation and Projection (UMAP) for unconstrained ordination and classification of biological indicators in aquatic ecology

Milošević, Djuradj; Medeiros, Andrew S.; Piperac, Milica Stojković; Cvijanović, Dušanka; Soininen, Janne; Milosavljević, Aleksandar; Predić, Bratislav

doi:10.1016/j.scitotenv.2021.152365

Cited by 26 publications

(22 citation statements)

References 48 publications

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“…Cells were separated into 23 clusters by FindClusters, by using the top 20 principle components and a resolution parameter of 1.2. For the clustering of GCs and macrophages, we set the resolution to 1.2 and applied the uniform mainfold approximation and projection (UMAP) algorithm to visualize cells in a two-dimensional space [ 32 , 33 ]. Batch effects between samples were removed for clustering by Harmony [ 34 ] (1.0) with the parameter group.by.vars set as ‘sample’.…”

Section: Methodsmentioning

confidence: 99%

Single-Cell Sequencing Reveals an Intrinsic Heterogeneity of the Preovulatory Follicular Microenvironment

Zhu

Zheng

et al. 2022

Biomolecules

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The follicular microenvironment, including intra-follicular granulosa cells (GCs), is responsible for oocyte maturation and subsequent ovulation. However, the functions of GCs and cellular components of the follicular microenvironment in preovulatory follicles have not been extensively explored. Here, we surveyed the single-cell transcriptome of the follicular microenvironment around MII oocytes in six human preovulatory follicles in in vitro fertilization. There were six different cell types in the preovulatory follicles, including GCs and various immune cells. In GCs, we identified nine different functional clusters with different functional transcriptomic profiles, including specific clusters involved in inflammatory responses and adhesive function. Follicular macrophages are involved in immune responses, extracellular matrix remoulding and assist GCs in promoting the oocyte meiotic resumption. Interestingly, we observed that the specific terminal state subcluster of GCs with high levels of adhesive-related molecules should result in macrophage recruitment and residence, further contributing to an obvious heterogeneity of the immune cell proportion in preovulatory follicles from different patients. Our results provide a comprehensive understanding of the transcriptomic landscape of the preovulatory follicular microenvironment at the single-cell level. It provides valuable insights into understanding the regulation of the oocyte maturation and ovulation process, offering potential clues for the diagnosis and treatment of oocyte-maturation-related and ovulation-related diseases.

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Section: Methodsmentioning

confidence: 99%

Single-Cell Sequencing Reveals an Intrinsic Heterogeneity of the Preovulatory Follicular Microenvironment

Zhu

Zheng

et al. 2022

Biomolecules

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“…We downloaded the original single-cell RNA sequencing data from five ATC patients and six PTC patients on the GEO database (GSE148673 and GSE191288). After standard data quality control, batch effect adjustment, and normalization using the “Seurat” package ( 26 ), we clustered all cells using the Uniform Manifold Approximation and Projection (UMAP) method ( 27 ) as four basic types: tumor/epithelial cells, immune cells, endothelial cells, and fibroblasts via cell markers ( Supplementary Figure 1A ). MMP1 expression was analyzed in PTC/ATC and all kinds of cells.…”

Section: Methodsmentioning

confidence: 99%

MMP1 acts as a potential regulator of tumor progression and dedifferentiation in papillary thyroid cancer

Zhou

Tan

et al. 2022

Front. Oncol.

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Papillary thyroid cancer (PTC) is one of the malignancies with an excellent prognosis. However, in PTC, progression or dedifferentiation into poorly differentiated thyroid cancer (PDTC) or anaplastic thyroid cancer (ATC) extremely jeopardizes patients’ prognosis. MMP1 is a zinc-dependent endopeptidase, and its role in PTC progression and dedifferentiation is unclear. In this study, transcriptome data of PDTC/ATC and PTC from the Gene Expression Omnibus and The Cancer Genome Atlas databases were utilized to perform an integrated analysis of MMP1 as a potential regulator of tumor progression and dedifferentiation in PTC. Both bulk and single-cell RNA-sequencing data confirmed the high expression of MMP1 in ATC tissues and cells, and further study verified that MMP1 possessed good diagnostic and prognostic value in PTC and PDTC/ATC. Up-regulated MMP1 was found to be positively related to more aggressive clinical characteristics, worse survival, extracellular matrix-related pathways, oncogenic immune microenvironment, more mutations, higher stemness, and more dedifferentiation of PTC. Meanwhile, in vitro experiments verified the high level of MMP1 in PDTC/ATC cell lines, and MMP1 knockdown and its inhibitor triolein could both inhibit the cell viability of PTC and PDTC/ATC. In conclusion, our findings suggest that MMP1 is a potential regulator of tumor progression and dedifferentiation in PTC, and might become a novel therapeutic target for PTC, especially for more aggressive PDTC and ATC.

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“…end while 13: end for UMAP UMAP is one of the most popular dimension reduction algorithms in the datascience field [35,36,37,38]. The algorithm consists of two primary steps: computing the similarity weights among the data and finding lower-dimensional embeddings that best match the computed similarity weights.…”

Section: Vaementioning

confidence: 99%

Similarity-assisted Variational Autoencoder forNonlinear Dimension Reduction with Applicationto Single-cell RNA Sequencing Data

Chun

Kim

2023

Preprint

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Background: Deep generative models naturally become nonlinear dimension reduction tools to visualize large-scale datasets such as single-cell RNA sequencing datasets for revealing latent grouping patterns or identifying outliers. The Variational autoencoder (VAE) is a popular deep generative method equipped with encoder/decoder structures. The encoder and decoder are useful when a new sample is mapped to the latent space and a data point is generated from a point in a latent space. However, the VAE tends not to show grouping pattern clearly without additional annotation information. On the other hand, similarity-based dimension reduction methods such as t-SNE or UMAP present clear grouping patterns even though these methods do not have encoder/decoder structures. To bridge this gap, we propose a new approach that adopts similarity information in the VAE framework. In addition, for biological applications, we extend our approach to a conditional VAE (CVAE) to account for covariate effects in the dimension reduction step. Results: In the simulation study and real single-cell RNA sequencing data analyses, our method shows great performance compared to existing state-of-the-art methods by producing clear grouping structures using an inferred encoder and decoder. Our method also successfully adjusts for covariate effects, resulting in more useful dimension reduction. Conclusions: Our method is able to produce clearer grouping patterns than those of other regularized VAE methods by utilizing similarity information encoded in the data via the highly celebrated UMAP loss function.

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The application of Uniform Manifold Approximation and Projection (UMAP) for unconstrained ordination and classification of biological indicators in aquatic ecology

Cited by 26 publications

References 48 publications

Single-Cell Sequencing Reveals an Intrinsic Heterogeneity of the Preovulatory Follicular Microenvironment

Single-Cell Sequencing Reveals an Intrinsic Heterogeneity of the Preovulatory Follicular Microenvironment

MMP1 acts as a potential regulator of tumor progression and dedifferentiation in papillary thyroid cancer

Similarity-assisted Variational Autoencoder forNonlinear Dimension Reduction with Applicationto Single-cell RNA Sequencing Data

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